Uncalibrated Visual Servoing in 3D Workspace

  • Paulo J. Sequeira Gonçalves
  • A. Paris
  • C. Christo
  • J. M. C. Sousa
  • J. R. Caldas Pinto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4142)


In this paper, inverse fuzzy models for uncalibrated visual servoing, in 3D Workspace, are developed and validated in a six degrees of freedom robotic manipulator. This approach does not require calibrated kinematic and camera models, as needed in classical visual servoing to obtain the Jacobian. Fuzzy modeling is used to identify the inverse Jacobian in the robot workspace. Robot control is achieved by means of using the inverse fuzzy models directly as the controller. Experimental results obtained in a PUMA robot performing eye-to-hand visual servoing demonstrate the validity of the approach.


Fuzzy Model Fuzzy Controller Fuzzy Cluster Inverse Model Robotic Manipulator 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Babuška, R.: Fuzzy Modeling for Control. Kluwer Academic Publishers, Boston (1998)Google Scholar
  2. 2.
    Cervera, E., Martinet, P.: Combining pixel and depth information in image-based visual servoing. In: Proceedings of the Ninth International Conference on Advanced Robotics, Tokyo, Japan, pp. 445–450 (1999)Google Scholar
  3. 3.
    Chaumette, F.: Potential problems of stability and convergence in image-based and position-based visual servoing. In: Kriegman, D., Hager, G., Morse, A.S. (eds.) The Confluence of Vision and Control. LNCIS Series (1998), pp. 66–78 (2000)Google Scholar
  4. 4.
    Dementhon, D., Davis, L.: Model-based object pose in 25 lines of code. International Journal of Computer Vision 15(1/2), 123–141 (1995)CrossRefGoogle Scholar
  5. 5.
    Espiau, B., Chaumette, F., Rives, P.: A new approach to visual servoing in robotics. IEEE Transactions on Robotics and Automation 8(3), 313–326 (1992)CrossRefGoogle Scholar
  6. 6.
    Gonçalves, P.J.S., Mendonça, L.F., Sousa, J.M.C., Pinto, J.R.C.: Image recognition applied to robot control using fuzzy modeling. In: Campilho, A.C., Kamel, M.S. (eds.) ICIAR 2004. LNCS, vol. 3211, pp. 253–260. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Gonçalves, P.S., Mendonça, L., Sousa, J., Pinto, J.C.: Improving visual servoing using fuzzy filters. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Budapest, Hungary, pp. 1185–1190 (2004)Google Scholar
  8. 8.
    Gonçalves, P.S., Pinto, J.C.: Camera configurations of a visual servoing setup, for a 2 dof planar robot. In: Proceedings of the 7th International IFAC Symposium on Robot Control, Wroclaw, Poland, pp. 181–187 (September 2003)Google Scholar
  9. 9.
    Guillaume, S.: Designing fuzzy inference systems from data: an interpretability-oriented review. IEEE Trans. on Fuzzy Systems 9(3), 426–443 (2001)CrossRefMathSciNetGoogle Scholar
  10. 10.
    Gustafson, D.E., Kessel, W.C.: Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings IEEE CDC, San Diego, USA, pp. 761–766 (1979)Google Scholar
  11. 11.
    Hosoda, K., Asada, M.: Versatile visual servoing without knowledge of true jacobian. In: Proceedings of the IEEE/RSJ/GI International Conference on Inteligent Robots and Systems, Munich, Germany, pp. 186–193 (1994)Google Scholar
  12. 12.
    Hutchinson, S., Hager, G., Corke, P.: A tutorial on visual servo control. IEEE Transactions on Robotics and Automation 12(5), 651–670 (1996)CrossRefGoogle Scholar
  13. 13.
    Jägersand, M., Fuentes, O., Nelson, R.: Experimental evaluation of uncalibrated visual servoing for precision manipulation. In: Proceedings of the IEEE International Conference on Robotics and Automation, pp. 2874–2880 (1997)Google Scholar
  14. 14.
    Malis, E., Chaumette, F., Boudet, S.: 2 \(\frac{1}{2}\) d visual servoing. IEEE Transactions on Robotics and Automation 15(2), 238–250 (1999)CrossRefGoogle Scholar
  15. 15.
    Peipmeier, J., McMurray, G., Lipkin, H.: Uncalibrated dynamic visual servoing. IEEE Trans. on Robotics and Automation 20(1), 143–147 (2004)CrossRefGoogle Scholar
  16. 16.
    Sousa, J.M., Kaymak, U.: Fuzzy Decision Making in Modeling and Control. World Scientific Pub. Co., Singapore (2002)MATHCrossRefGoogle Scholar
  17. 17.
    Sousa, J., Silva, C., da Costa, J.S.: Fuzzy active noise modeling and control. International Journal of Approximate Reasoning 33, 51–70 (2003)MATHCrossRefGoogle Scholar
  18. 18.
    Suh, I.H., Kim, T.W.: Fuzzy membership function based neural networks with applications to the visual servoing of robot manipulators. IEEE Transactions on Fuzzy Systems 2(3), 203–220 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Paulo J. Sequeira Gonçalves
    • 1
    • 2
  • A. Paris
    • 2
  • C. Christo
    • 2
  • J. M. C. Sousa
    • 2
  • J. R. Caldas Pinto
    • 2
  1. 1.Dept. of Industrial EngineeringInstituto Politécnico de Castelo Branco, Escola Superior de TecnologiaCastelo BrancoPortugal
  2. 2.Dept. of Mechanical Engineering, GCAR/IDMECTechnical University of Lisbon, Instituto Superior TécnicoLisboaPortugal

Personalised recommendations